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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 18 Dec 2014 10:54:09 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/18/t1418900204oip73sxmqagqa7w.htm/, Retrieved Sun, 19 May 2024 19:17:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270795, Retrieved Sun, 19 May 2024 19:17:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact115

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Verklaring examen...] [2014-12-18 10:27:57] [94e0b03eaaae24ea322c1a0c8a3c30a1]
-    D    [Multiple Regression] [Verband tussen ex...] [2014-12-18 10:54:09] [0adf43ccf8dfa476608a94fd7836e72e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	149	96	18	68
12	139	70	31	39
13	148	88	39	32
7	158	114	46	62
7	128	69	31	33
13	224	176	67	52
15	159	114	35	62
13	105	121	52	77
11	159	110	77	76
8	167	158	37	41
11	165	116	32	48
6	159	181	36	63
11	119	77	38	30
12	176	141	69	78
6	54	35	21	19
11	91	80	26	31
12	163	152	54	66
9	124	97	36	35
10	137	99	42	42
10	121	84	23	45
14	148	101	112	25
11	221	107	35	44
14	188	88	47	69
12	149	112	47	54
11	244	171	37	74
16	148	137	109	80
13	92	77	24	42
10	150	66	20	61
12	153	93	22	41
8	94	105	23	46
12	156	131	32	39
9	132	102	30	34
10	161	161	92	51
11	105	120	43	42
10	97	127	55	31
10	151	77	16	39
13	131	108	49	20
9	166	85	71	49
12	157	168	43	53
6	111	48	29	31
11	145	152	56	39
13	162	75	46	54
11	163	107	19	49
12	59	62	23	34
11	187	121	59	46
12	109	124	30	55
8	90	72	61	42
13	105	40	7	50
12	83	58	38	13
12	116	97	32	37
7	42	88	16	25
11	148	126	19	30
12	155	104	22	28
13	125	148	48	45
10	116	146	23	35
6	128	80	26	28
14	138	97	33	41
8	49	25	9	6
13	96	99	24	45
9	164	118	34	73
13	162	58	48	17
8	99	63	18	40
16	202	139	43	64
9	186	50	33	37
9	66	60	28	25
11	183	152	71	65
13	214	142	26	100
15	188	94	67	28
12	104	66	34	35
12	177	127	80	56
11	126	67	29	29
9	76	90	16	43
15	99	75	59	59
13	139	128	32	50
13	162	146	43	59
13	108	69	38	27
13	159	186	29	61
10	74	81	36	28
8	110	85	32	51
11	96	54	35	35
13	116	46	21	29
11	87	106	29	48
4	97	34	12	25
10	127	60	37	44
12	106	95	37	64
11	80	57	47	32
11	74	62	51	20
9	91	36	32	28
13	133	56	21	34
13	74	54	13	31
6	114	64	14	26
10	140	76	-2	58
9	95	98	20	23
8	98	88	24	21
9	121	35	11	21
7	126	102	23	33
11	98	61	24	16
14	95	80	14	20
8	110	49	52	37
11	70	78	15	35
10	102	90	23	33
10	130	55	35	41
14	96	96	24	40
9	102	43	39	35
14	100	52	29	28
6	52	54	8	22
10	98	51	18	44
12	118	51	24	27
11	99	38	19	17

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270795&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270795&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270795&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 8.04356 + 0.00837115LFM[t] -0.000890466B[t] + 0.0260168PRH[t] + 0.0211985CH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  8.04356 +  0.00837115LFM[t] -0.000890466B[t] +  0.0260168PRH[t] +  0.0211985CH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270795&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  8.04356 +  0.00837115LFM[t] -0.000890466B[t] +  0.0260168PRH[t] +  0.0211985CH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270795&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270795&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 8.04356 + 0.00837115LFM[t] -0.000890466B[t] + 0.0260168PRH[t] + 0.0211985CH[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.043560.76070310.573.51434e-181.75717e-18
LFM0.008371150.007455381.1230.2640940.132047
B-0.0008904660.00813861-0.10940.9130860.456543
PRH0.02601680.01253382.0760.04038490.0201925
CH0.02119850.01723311.230.2214330.110717

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 8.04356 & 0.760703 & 10.57 & 3.51434e-18 & 1.75717e-18 \tabularnewline
LFM & 0.00837115 & 0.00745538 & 1.123 & 0.264094 & 0.132047 \tabularnewline
B & -0.000890466 & 0.00813861 & -0.1094 & 0.913086 & 0.456543 \tabularnewline
PRH & 0.0260168 & 0.0125338 & 2.076 & 0.0403849 & 0.0201925 \tabularnewline
CH & 0.0211985 & 0.0172331 & 1.23 & 0.221433 & 0.110717 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270795&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]8.04356[/C][C]0.760703[/C][C]10.57[/C][C]3.51434e-18[/C][C]1.75717e-18[/C][/ROW]
[ROW][C]LFM[/C][C]0.00837115[/C][C]0.00745538[/C][C]1.123[/C][C]0.264094[/C][C]0.132047[/C][/ROW]
[ROW][C]B[/C][C]-0.000890466[/C][C]0.00813861[/C][C]-0.1094[/C][C]0.913086[/C][C]0.456543[/C][/ROW]
[ROW][C]PRH[/C][C]0.0260168[/C][C]0.0125338[/C][C]2.076[/C][C]0.0403849[/C][C]0.0201925[/C][/ROW]
[ROW][C]CH[/C][C]0.0211985[/C][C]0.0172331[/C][C]1.23[/C][C]0.221433[/C][C]0.110717[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270795&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270795&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.043560.76070310.573.51434e-181.75717e-18
LFM0.008371150.007455381.1230.2640940.132047
B-0.0008904660.00813861-0.10940.9130860.456543
PRH0.02601680.01253382.0760.04038490.0201925
CH0.02119850.01723311.230.2214330.110717






Multiple Linear Regression - Regression Statistics
Multiple R0.385564
R-squared0.14866
Adjusted R-squared0.115916
F-TEST (value)4.54009
F-TEST (DF numerator)4
F-TEST (DF denominator)104
p-value0.00202484
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.27639
Sum Squared Residuals538.922

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.385564 \tabularnewline
R-squared & 0.14866 \tabularnewline
Adjusted R-squared & 0.115916 \tabularnewline
F-TEST (value) & 4.54009 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 104 \tabularnewline
p-value & 0.00202484 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.27639 \tabularnewline
Sum Squared Residuals & 538.922 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270795&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.385564[/C][/ROW]
[ROW][C]R-squared[/C][C]0.14866[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.115916[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.54009[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]104[/C][/ROW]
[ROW][C]p-value[/C][C]0.00202484[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.27639[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]538.922[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270795&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270795&T=3

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.385564
R-squared0.14866
Adjusted R-squared0.115916
F-TEST (value)4.54009
F-TEST (DF numerator)4
F-TEST (DF denominator)104
p-value0.00202484
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.27639
Sum Squared Residuals538.922






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11311.11521.88483
21210.77811.22192
31310.89712.10286
4711.7758-4.77577
5710.5597-3.5597
61312.60740.392577
71511.4983.50205
81311.79991.20006
91112.891-1.891
10811.1326-3.13261
111111.1716-0.17157
12611.4855-5.48551
131110.59580.404247
141212.84-0.839968
1569.41356-3.41356
161110.06770.932314
171212.0767-0.0767137
18910.6738-1.67376
191011.0853-1.08529
201010.534-0.533987
211412.63641.3636
221111.6416-0.641625
231412.22451.77554
241211.55860.441363
251112.4652-1.46516
261613.69222.30779
271310.25992.74012
281011.0539-1.0539
291210.6831.31696
30810.3105-2.31046
311210.89211.10791
32910.559-1.55898
331012.7226-2.72262
341110.82470.175267
351010.8305-0.83055
361010.482-0.482046
371310.74282.2572
38912.2434-3.2434
391211.45050.549526
40610.3417-4.34165
411111.4057-0.405708
421311.67441.32561
431110.84580.154179
44129.801382.19862
451112.0113-1.01134
461210.7921.20798
47811.2102-3.21021
481310.1292.87105
49129.950942.04906
501210.54511.45488
5179.26302-2.26302
521110.30060.699436
531210.41441.58559
541311.16091.8391
551010.2249-0.224938
56610.3138-4.31382
571410.84013.1599
5888.79283-0.792825
591310.33742.66263
60911.7434-2.74341
611310.95722.04278
62810.1324-2.13244
631612.08623.91382
64911.199-2.19897
6599.80106-0.80106
661112.6652-1.66522
671312.50480.495176
681511.87033.12968
691210.48191.51809
701212.6806-0.680625
711110.40790.592094
7299.92743-0.927428
731511.59123.40878
741310.98562.01437
751311.63911.36089
761310.44722.5528
771311.25651.74346
781010.1211-0.121059
79810.8024-2.80236
801110.45160.54836
811310.13482.86524
821110.44950.550526
8349.66745-5.66745
841010.9486-0.948622
851211.16560.834369
861110.56360.436364
871110.35860.641357
88910.1994-1.19937
891310.37422.62584
90139.610313.38969
9169.85628-3.85628
921010.3253-0.325322
9399.75945-0.759454
9489.85514-1.85514
9599.75665-0.756654
96710.3054-3.30543
97119.773191.22681
98149.555794.44421
99811.058-3.05797
100119.692281.30772
1011010.1152-0.115211
1021010.8626-0.862559
1031410.2343.76595
104910.6157-1.61573
1051410.18243.81759
10669.10527-3.10527
1071010.2196-0.219553
1081210.18271.8173
109119.693161.30684

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 11.1152 & 1.88483 \tabularnewline
2 & 12 & 10.7781 & 1.22192 \tabularnewline
3 & 13 & 10.8971 & 2.10286 \tabularnewline
4 & 7 & 11.7758 & -4.77577 \tabularnewline
5 & 7 & 10.5597 & -3.5597 \tabularnewline
6 & 13 & 12.6074 & 0.392577 \tabularnewline
7 & 15 & 11.498 & 3.50205 \tabularnewline
8 & 13 & 11.7999 & 1.20006 \tabularnewline
9 & 11 & 12.891 & -1.891 \tabularnewline
10 & 8 & 11.1326 & -3.13261 \tabularnewline
11 & 11 & 11.1716 & -0.17157 \tabularnewline
12 & 6 & 11.4855 & -5.48551 \tabularnewline
13 & 11 & 10.5958 & 0.404247 \tabularnewline
14 & 12 & 12.84 & -0.839968 \tabularnewline
15 & 6 & 9.41356 & -3.41356 \tabularnewline
16 & 11 & 10.0677 & 0.932314 \tabularnewline
17 & 12 & 12.0767 & -0.0767137 \tabularnewline
18 & 9 & 10.6738 & -1.67376 \tabularnewline
19 & 10 & 11.0853 & -1.08529 \tabularnewline
20 & 10 & 10.534 & -0.533987 \tabularnewline
21 & 14 & 12.6364 & 1.3636 \tabularnewline
22 & 11 & 11.6416 & -0.641625 \tabularnewline
23 & 14 & 12.2245 & 1.77554 \tabularnewline
24 & 12 & 11.5586 & 0.441363 \tabularnewline
25 & 11 & 12.4652 & -1.46516 \tabularnewline
26 & 16 & 13.6922 & 2.30779 \tabularnewline
27 & 13 & 10.2599 & 2.74012 \tabularnewline
28 & 10 & 11.0539 & -1.0539 \tabularnewline
29 & 12 & 10.683 & 1.31696 \tabularnewline
30 & 8 & 10.3105 & -2.31046 \tabularnewline
31 & 12 & 10.8921 & 1.10791 \tabularnewline
32 & 9 & 10.559 & -1.55898 \tabularnewline
33 & 10 & 12.7226 & -2.72262 \tabularnewline
34 & 11 & 10.8247 & 0.175267 \tabularnewline
35 & 10 & 10.8305 & -0.83055 \tabularnewline
36 & 10 & 10.482 & -0.482046 \tabularnewline
37 & 13 & 10.7428 & 2.2572 \tabularnewline
38 & 9 & 12.2434 & -3.2434 \tabularnewline
39 & 12 & 11.4505 & 0.549526 \tabularnewline
40 & 6 & 10.3417 & -4.34165 \tabularnewline
41 & 11 & 11.4057 & -0.405708 \tabularnewline
42 & 13 & 11.6744 & 1.32561 \tabularnewline
43 & 11 & 10.8458 & 0.154179 \tabularnewline
44 & 12 & 9.80138 & 2.19862 \tabularnewline
45 & 11 & 12.0113 & -1.01134 \tabularnewline
46 & 12 & 10.792 & 1.20798 \tabularnewline
47 & 8 & 11.2102 & -3.21021 \tabularnewline
48 & 13 & 10.129 & 2.87105 \tabularnewline
49 & 12 & 9.95094 & 2.04906 \tabularnewline
50 & 12 & 10.5451 & 1.45488 \tabularnewline
51 & 7 & 9.26302 & -2.26302 \tabularnewline
52 & 11 & 10.3006 & 0.699436 \tabularnewline
53 & 12 & 10.4144 & 1.58559 \tabularnewline
54 & 13 & 11.1609 & 1.8391 \tabularnewline
55 & 10 & 10.2249 & -0.224938 \tabularnewline
56 & 6 & 10.3138 & -4.31382 \tabularnewline
57 & 14 & 10.8401 & 3.1599 \tabularnewline
58 & 8 & 8.79283 & -0.792825 \tabularnewline
59 & 13 & 10.3374 & 2.66263 \tabularnewline
60 & 9 & 11.7434 & -2.74341 \tabularnewline
61 & 13 & 10.9572 & 2.04278 \tabularnewline
62 & 8 & 10.1324 & -2.13244 \tabularnewline
63 & 16 & 12.0862 & 3.91382 \tabularnewline
64 & 9 & 11.199 & -2.19897 \tabularnewline
65 & 9 & 9.80106 & -0.80106 \tabularnewline
66 & 11 & 12.6652 & -1.66522 \tabularnewline
67 & 13 & 12.5048 & 0.495176 \tabularnewline
68 & 15 & 11.8703 & 3.12968 \tabularnewline
69 & 12 & 10.4819 & 1.51809 \tabularnewline
70 & 12 & 12.6806 & -0.680625 \tabularnewline
71 & 11 & 10.4079 & 0.592094 \tabularnewline
72 & 9 & 9.92743 & -0.927428 \tabularnewline
73 & 15 & 11.5912 & 3.40878 \tabularnewline
74 & 13 & 10.9856 & 2.01437 \tabularnewline
75 & 13 & 11.6391 & 1.36089 \tabularnewline
76 & 13 & 10.4472 & 2.5528 \tabularnewline
77 & 13 & 11.2565 & 1.74346 \tabularnewline
78 & 10 & 10.1211 & -0.121059 \tabularnewline
79 & 8 & 10.8024 & -2.80236 \tabularnewline
80 & 11 & 10.4516 & 0.54836 \tabularnewline
81 & 13 & 10.1348 & 2.86524 \tabularnewline
82 & 11 & 10.4495 & 0.550526 \tabularnewline
83 & 4 & 9.66745 & -5.66745 \tabularnewline
84 & 10 & 10.9486 & -0.948622 \tabularnewline
85 & 12 & 11.1656 & 0.834369 \tabularnewline
86 & 11 & 10.5636 & 0.436364 \tabularnewline
87 & 11 & 10.3586 & 0.641357 \tabularnewline
88 & 9 & 10.1994 & -1.19937 \tabularnewline
89 & 13 & 10.3742 & 2.62584 \tabularnewline
90 & 13 & 9.61031 & 3.38969 \tabularnewline
91 & 6 & 9.85628 & -3.85628 \tabularnewline
92 & 10 & 10.3253 & -0.325322 \tabularnewline
93 & 9 & 9.75945 & -0.759454 \tabularnewline
94 & 8 & 9.85514 & -1.85514 \tabularnewline
95 & 9 & 9.75665 & -0.756654 \tabularnewline
96 & 7 & 10.3054 & -3.30543 \tabularnewline
97 & 11 & 9.77319 & 1.22681 \tabularnewline
98 & 14 & 9.55579 & 4.44421 \tabularnewline
99 & 8 & 11.058 & -3.05797 \tabularnewline
100 & 11 & 9.69228 & 1.30772 \tabularnewline
101 & 10 & 10.1152 & -0.115211 \tabularnewline
102 & 10 & 10.8626 & -0.862559 \tabularnewline
103 & 14 & 10.234 & 3.76595 \tabularnewline
104 & 9 & 10.6157 & -1.61573 \tabularnewline
105 & 14 & 10.1824 & 3.81759 \tabularnewline
106 & 6 & 9.10527 & -3.10527 \tabularnewline
107 & 10 & 10.2196 & -0.219553 \tabularnewline
108 & 12 & 10.1827 & 1.8173 \tabularnewline
109 & 11 & 9.69316 & 1.30684 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270795&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]11.1152[/C][C]1.88483[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]10.7781[/C][C]1.22192[/C][/ROW]
[ROW][C]3[/C][C]13[/C][C]10.8971[/C][C]2.10286[/C][/ROW]
[ROW][C]4[/C][C]7[/C][C]11.7758[/C][C]-4.77577[/C][/ROW]
[ROW][C]5[/C][C]7[/C][C]10.5597[/C][C]-3.5597[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]12.6074[/C][C]0.392577[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]11.498[/C][C]3.50205[/C][/ROW]
[ROW][C]8[/C][C]13[/C][C]11.7999[/C][C]1.20006[/C][/ROW]
[ROW][C]9[/C][C]11[/C][C]12.891[/C][C]-1.891[/C][/ROW]
[ROW][C]10[/C][C]8[/C][C]11.1326[/C][C]-3.13261[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]11.1716[/C][C]-0.17157[/C][/ROW]
[ROW][C]12[/C][C]6[/C][C]11.4855[/C][C]-5.48551[/C][/ROW]
[ROW][C]13[/C][C]11[/C][C]10.5958[/C][C]0.404247[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]12.84[/C][C]-0.839968[/C][/ROW]
[ROW][C]15[/C][C]6[/C][C]9.41356[/C][C]-3.41356[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]10.0677[/C][C]0.932314[/C][/ROW]
[ROW][C]17[/C][C]12[/C][C]12.0767[/C][C]-0.0767137[/C][/ROW]
[ROW][C]18[/C][C]9[/C][C]10.6738[/C][C]-1.67376[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]11.0853[/C][C]-1.08529[/C][/ROW]
[ROW][C]20[/C][C]10[/C][C]10.534[/C][C]-0.533987[/C][/ROW]
[ROW][C]21[/C][C]14[/C][C]12.6364[/C][C]1.3636[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]11.6416[/C][C]-0.641625[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]12.2245[/C][C]1.77554[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]11.5586[/C][C]0.441363[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]12.4652[/C][C]-1.46516[/C][/ROW]
[ROW][C]26[/C][C]16[/C][C]13.6922[/C][C]2.30779[/C][/ROW]
[ROW][C]27[/C][C]13[/C][C]10.2599[/C][C]2.74012[/C][/ROW]
[ROW][C]28[/C][C]10[/C][C]11.0539[/C][C]-1.0539[/C][/ROW]
[ROW][C]29[/C][C]12[/C][C]10.683[/C][C]1.31696[/C][/ROW]
[ROW][C]30[/C][C]8[/C][C]10.3105[/C][C]-2.31046[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]10.8921[/C][C]1.10791[/C][/ROW]
[ROW][C]32[/C][C]9[/C][C]10.559[/C][C]-1.55898[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]12.7226[/C][C]-2.72262[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]10.8247[/C][C]0.175267[/C][/ROW]
[ROW][C]35[/C][C]10[/C][C]10.8305[/C][C]-0.83055[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]10.482[/C][C]-0.482046[/C][/ROW]
[ROW][C]37[/C][C]13[/C][C]10.7428[/C][C]2.2572[/C][/ROW]
[ROW][C]38[/C][C]9[/C][C]12.2434[/C][C]-3.2434[/C][/ROW]
[ROW][C]39[/C][C]12[/C][C]11.4505[/C][C]0.549526[/C][/ROW]
[ROW][C]40[/C][C]6[/C][C]10.3417[/C][C]-4.34165[/C][/ROW]
[ROW][C]41[/C][C]11[/C][C]11.4057[/C][C]-0.405708[/C][/ROW]
[ROW][C]42[/C][C]13[/C][C]11.6744[/C][C]1.32561[/C][/ROW]
[ROW][C]43[/C][C]11[/C][C]10.8458[/C][C]0.154179[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]9.80138[/C][C]2.19862[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]12.0113[/C][C]-1.01134[/C][/ROW]
[ROW][C]46[/C][C]12[/C][C]10.792[/C][C]1.20798[/C][/ROW]
[ROW][C]47[/C][C]8[/C][C]11.2102[/C][C]-3.21021[/C][/ROW]
[ROW][C]48[/C][C]13[/C][C]10.129[/C][C]2.87105[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]9.95094[/C][C]2.04906[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]10.5451[/C][C]1.45488[/C][/ROW]
[ROW][C]51[/C][C]7[/C][C]9.26302[/C][C]-2.26302[/C][/ROW]
[ROW][C]52[/C][C]11[/C][C]10.3006[/C][C]0.699436[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]10.4144[/C][C]1.58559[/C][/ROW]
[ROW][C]54[/C][C]13[/C][C]11.1609[/C][C]1.8391[/C][/ROW]
[ROW][C]55[/C][C]10[/C][C]10.2249[/C][C]-0.224938[/C][/ROW]
[ROW][C]56[/C][C]6[/C][C]10.3138[/C][C]-4.31382[/C][/ROW]
[ROW][C]57[/C][C]14[/C][C]10.8401[/C][C]3.1599[/C][/ROW]
[ROW][C]58[/C][C]8[/C][C]8.79283[/C][C]-0.792825[/C][/ROW]
[ROW][C]59[/C][C]13[/C][C]10.3374[/C][C]2.66263[/C][/ROW]
[ROW][C]60[/C][C]9[/C][C]11.7434[/C][C]-2.74341[/C][/ROW]
[ROW][C]61[/C][C]13[/C][C]10.9572[/C][C]2.04278[/C][/ROW]
[ROW][C]62[/C][C]8[/C][C]10.1324[/C][C]-2.13244[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]12.0862[/C][C]3.91382[/C][/ROW]
[ROW][C]64[/C][C]9[/C][C]11.199[/C][C]-2.19897[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]9.80106[/C][C]-0.80106[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]12.6652[/C][C]-1.66522[/C][/ROW]
[ROW][C]67[/C][C]13[/C][C]12.5048[/C][C]0.495176[/C][/ROW]
[ROW][C]68[/C][C]15[/C][C]11.8703[/C][C]3.12968[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]10.4819[/C][C]1.51809[/C][/ROW]
[ROW][C]70[/C][C]12[/C][C]12.6806[/C][C]-0.680625[/C][/ROW]
[ROW][C]71[/C][C]11[/C][C]10.4079[/C][C]0.592094[/C][/ROW]
[ROW][C]72[/C][C]9[/C][C]9.92743[/C][C]-0.927428[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]11.5912[/C][C]3.40878[/C][/ROW]
[ROW][C]74[/C][C]13[/C][C]10.9856[/C][C]2.01437[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]11.6391[/C][C]1.36089[/C][/ROW]
[ROW][C]76[/C][C]13[/C][C]10.4472[/C][C]2.5528[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]11.2565[/C][C]1.74346[/C][/ROW]
[ROW][C]78[/C][C]10[/C][C]10.1211[/C][C]-0.121059[/C][/ROW]
[ROW][C]79[/C][C]8[/C][C]10.8024[/C][C]-2.80236[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]10.4516[/C][C]0.54836[/C][/ROW]
[ROW][C]81[/C][C]13[/C][C]10.1348[/C][C]2.86524[/C][/ROW]
[ROW][C]82[/C][C]11[/C][C]10.4495[/C][C]0.550526[/C][/ROW]
[ROW][C]83[/C][C]4[/C][C]9.66745[/C][C]-5.66745[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]10.9486[/C][C]-0.948622[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]11.1656[/C][C]0.834369[/C][/ROW]
[ROW][C]86[/C][C]11[/C][C]10.5636[/C][C]0.436364[/C][/ROW]
[ROW][C]87[/C][C]11[/C][C]10.3586[/C][C]0.641357[/C][/ROW]
[ROW][C]88[/C][C]9[/C][C]10.1994[/C][C]-1.19937[/C][/ROW]
[ROW][C]89[/C][C]13[/C][C]10.3742[/C][C]2.62584[/C][/ROW]
[ROW][C]90[/C][C]13[/C][C]9.61031[/C][C]3.38969[/C][/ROW]
[ROW][C]91[/C][C]6[/C][C]9.85628[/C][C]-3.85628[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]10.3253[/C][C]-0.325322[/C][/ROW]
[ROW][C]93[/C][C]9[/C][C]9.75945[/C][C]-0.759454[/C][/ROW]
[ROW][C]94[/C][C]8[/C][C]9.85514[/C][C]-1.85514[/C][/ROW]
[ROW][C]95[/C][C]9[/C][C]9.75665[/C][C]-0.756654[/C][/ROW]
[ROW][C]96[/C][C]7[/C][C]10.3054[/C][C]-3.30543[/C][/ROW]
[ROW][C]97[/C][C]11[/C][C]9.77319[/C][C]1.22681[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]9.55579[/C][C]4.44421[/C][/ROW]
[ROW][C]99[/C][C]8[/C][C]11.058[/C][C]-3.05797[/C][/ROW]
[ROW][C]100[/C][C]11[/C][C]9.69228[/C][C]1.30772[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]10.1152[/C][C]-0.115211[/C][/ROW]
[ROW][C]102[/C][C]10[/C][C]10.8626[/C][C]-0.862559[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]10.234[/C][C]3.76595[/C][/ROW]
[ROW][C]104[/C][C]9[/C][C]10.6157[/C][C]-1.61573[/C][/ROW]
[ROW][C]105[/C][C]14[/C][C]10.1824[/C][C]3.81759[/C][/ROW]
[ROW][C]106[/C][C]6[/C][C]9.10527[/C][C]-3.10527[/C][/ROW]
[ROW][C]107[/C][C]10[/C][C]10.2196[/C][C]-0.219553[/C][/ROW]
[ROW][C]108[/C][C]12[/C][C]10.1827[/C][C]1.8173[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]9.69316[/C][C]1.30684[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270795&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270795&T=4

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11311.11521.88483
21210.77811.22192
31310.89712.10286
4711.7758-4.77577
5710.5597-3.5597
61312.60740.392577
71511.4983.50205
81311.79991.20006
91112.891-1.891
10811.1326-3.13261
111111.1716-0.17157
12611.4855-5.48551
131110.59580.404247
141212.84-0.839968
1569.41356-3.41356
161110.06770.932314
171212.0767-0.0767137
18910.6738-1.67376
191011.0853-1.08529
201010.534-0.533987
211412.63641.3636
221111.6416-0.641625
231412.22451.77554
241211.55860.441363
251112.4652-1.46516
261613.69222.30779
271310.25992.74012
281011.0539-1.0539
291210.6831.31696
30810.3105-2.31046
311210.89211.10791
32910.559-1.55898
331012.7226-2.72262
341110.82470.175267
351010.8305-0.83055
361010.482-0.482046
371310.74282.2572
38912.2434-3.2434
391211.45050.549526
40610.3417-4.34165
411111.4057-0.405708
421311.67441.32561
431110.84580.154179
44129.801382.19862
451112.0113-1.01134
461210.7921.20798
47811.2102-3.21021
481310.1292.87105
49129.950942.04906
501210.54511.45488
5179.26302-2.26302
521110.30060.699436
531210.41441.58559
541311.16091.8391
551010.2249-0.224938
56610.3138-4.31382
571410.84013.1599
5888.79283-0.792825
591310.33742.66263
60911.7434-2.74341
611310.95722.04278
62810.1324-2.13244
631612.08623.91382
64911.199-2.19897
6599.80106-0.80106
661112.6652-1.66522
671312.50480.495176
681511.87033.12968
691210.48191.51809
701212.6806-0.680625
711110.40790.592094
7299.92743-0.927428
731511.59123.40878
741310.98562.01437
751311.63911.36089
761310.44722.5528
771311.25651.74346
781010.1211-0.121059
79810.8024-2.80236
801110.45160.54836
811310.13482.86524
821110.44950.550526
8349.66745-5.66745
841010.9486-0.948622
851211.16560.834369
861110.56360.436364
871110.35860.641357
88910.1994-1.19937
891310.37422.62584
90139.610313.38969
9169.85628-3.85628
921010.3253-0.325322
9399.75945-0.759454
9489.85514-1.85514
9599.75665-0.756654
96710.3054-3.30543
97119.773191.22681
98149.555794.44421
99811.058-3.05797
100119.692281.30772
1011010.1152-0.115211
1021010.8626-0.862559
1031410.2343.76595
104910.6157-1.61573
1051410.18243.81759
10669.10527-3.10527
1071010.2196-0.219553
1081210.18271.8173
109119.693161.30684






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.9439110.1121780.0560892
90.9531030.09379360.0468968
100.9658280.06834320.0341716
110.9380630.1238730.0619366
120.9664310.06713850.0335693
130.9503030.09939440.0496972
140.921590.156820.0784102
150.906820.186360.0931799
160.913720.1725610.0862803
170.88730.2253990.1127
180.8482820.3034350.151718
190.7988480.4023040.201152
200.7402250.5195510.259775
210.7281680.5436640.271832
220.6889430.6221150.311057
230.6297810.7404390.370219
240.5682150.863570.431785
250.5171630.9656740.482837
260.4903930.9807860.509607
270.5683070.8633860.431693
280.5361850.9276290.463815
290.5080820.9838360.491918
300.4709730.9419470.529027
310.4671150.934230.532885
320.419230.8384590.58077
330.4183890.8367780.581611
340.3778050.7556110.622195
350.3293360.6586710.670664
360.2769080.5538160.723092
370.3011080.6022170.698892
380.4031060.8062120.596894
390.371520.743040.62848
400.5172870.9654260.482713
410.4663530.9327060.533647
420.4258670.8517350.574133
430.3727890.7455770.627211
440.3834050.7668090.616595
450.3454750.6909490.654525
460.3122980.6245950.687702
470.3687010.7374010.631299
480.4153820.8307640.584618
490.4061730.8123450.593827
500.3734740.7469470.626526
510.3685850.7371710.631415
520.3264760.6529530.673524
530.2991030.5982050.700897
540.2827940.5655890.717206
550.244260.4885190.75574
560.3840780.7681550.615922
570.4231230.8462470.576877
580.3725770.7451540.627423
590.385690.771380.61431
600.4142040.8284070.585796
610.392070.7841410.60793
620.381030.762060.61897
630.4605950.921190.539405
640.4522510.9045020.547749
650.402750.80550.59725
660.4113090.8226190.588691
670.3560060.7120110.643994
680.3705210.7410410.629479
690.3364450.6728910.663555
700.309520.619040.69048
710.2605370.5210750.739463
720.2232340.4464680.776766
730.2613340.5226670.738666
740.2358480.4716960.764152
750.1984560.3969120.801544
760.2014590.4029170.798541
770.1731860.3463720.826814
780.1364880.2729770.863512
790.1509720.3019440.849028
800.1179570.2359150.882043
810.1414360.2828720.858564
820.1088210.2176430.891179
830.3099890.6199780.690011
840.2552540.5105080.744746
850.2083360.4166720.791664
860.1627790.3255590.837221
870.1270480.2540970.872952
880.09990230.1998050.900098
890.1130360.2260710.886964
900.1258670.2517350.874133
910.2045060.4090120.795494
920.155420.310840.84458
930.1195210.2390420.880479
940.1234710.2469410.876529
950.1006730.2013460.899327
960.5115130.9769740.488487
970.4178170.8356340.582183
980.3509730.7019460.649027
990.2975420.5950840.702458
1000.2202230.4404470.779777
1010.287310.5746210.71269

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.943911 & 0.112178 & 0.0560892 \tabularnewline
9 & 0.953103 & 0.0937936 & 0.0468968 \tabularnewline
10 & 0.965828 & 0.0683432 & 0.0341716 \tabularnewline
11 & 0.938063 & 0.123873 & 0.0619366 \tabularnewline
12 & 0.966431 & 0.0671385 & 0.0335693 \tabularnewline
13 & 0.950303 & 0.0993944 & 0.0496972 \tabularnewline
14 & 0.92159 & 0.15682 & 0.0784102 \tabularnewline
15 & 0.90682 & 0.18636 & 0.0931799 \tabularnewline
16 & 0.91372 & 0.172561 & 0.0862803 \tabularnewline
17 & 0.8873 & 0.225399 & 0.1127 \tabularnewline
18 & 0.848282 & 0.303435 & 0.151718 \tabularnewline
19 & 0.798848 & 0.402304 & 0.201152 \tabularnewline
20 & 0.740225 & 0.519551 & 0.259775 \tabularnewline
21 & 0.728168 & 0.543664 & 0.271832 \tabularnewline
22 & 0.688943 & 0.622115 & 0.311057 \tabularnewline
23 & 0.629781 & 0.740439 & 0.370219 \tabularnewline
24 & 0.568215 & 0.86357 & 0.431785 \tabularnewline
25 & 0.517163 & 0.965674 & 0.482837 \tabularnewline
26 & 0.490393 & 0.980786 & 0.509607 \tabularnewline
27 & 0.568307 & 0.863386 & 0.431693 \tabularnewline
28 & 0.536185 & 0.927629 & 0.463815 \tabularnewline
29 & 0.508082 & 0.983836 & 0.491918 \tabularnewline
30 & 0.470973 & 0.941947 & 0.529027 \tabularnewline
31 & 0.467115 & 0.93423 & 0.532885 \tabularnewline
32 & 0.41923 & 0.838459 & 0.58077 \tabularnewline
33 & 0.418389 & 0.836778 & 0.581611 \tabularnewline
34 & 0.377805 & 0.755611 & 0.622195 \tabularnewline
35 & 0.329336 & 0.658671 & 0.670664 \tabularnewline
36 & 0.276908 & 0.553816 & 0.723092 \tabularnewline
37 & 0.301108 & 0.602217 & 0.698892 \tabularnewline
38 & 0.403106 & 0.806212 & 0.596894 \tabularnewline
39 & 0.37152 & 0.74304 & 0.62848 \tabularnewline
40 & 0.517287 & 0.965426 & 0.482713 \tabularnewline
41 & 0.466353 & 0.932706 & 0.533647 \tabularnewline
42 & 0.425867 & 0.851735 & 0.574133 \tabularnewline
43 & 0.372789 & 0.745577 & 0.627211 \tabularnewline
44 & 0.383405 & 0.766809 & 0.616595 \tabularnewline
45 & 0.345475 & 0.690949 & 0.654525 \tabularnewline
46 & 0.312298 & 0.624595 & 0.687702 \tabularnewline
47 & 0.368701 & 0.737401 & 0.631299 \tabularnewline
48 & 0.415382 & 0.830764 & 0.584618 \tabularnewline
49 & 0.406173 & 0.812345 & 0.593827 \tabularnewline
50 & 0.373474 & 0.746947 & 0.626526 \tabularnewline
51 & 0.368585 & 0.737171 & 0.631415 \tabularnewline
52 & 0.326476 & 0.652953 & 0.673524 \tabularnewline
53 & 0.299103 & 0.598205 & 0.700897 \tabularnewline
54 & 0.282794 & 0.565589 & 0.717206 \tabularnewline
55 & 0.24426 & 0.488519 & 0.75574 \tabularnewline
56 & 0.384078 & 0.768155 & 0.615922 \tabularnewline
57 & 0.423123 & 0.846247 & 0.576877 \tabularnewline
58 & 0.372577 & 0.745154 & 0.627423 \tabularnewline
59 & 0.38569 & 0.77138 & 0.61431 \tabularnewline
60 & 0.414204 & 0.828407 & 0.585796 \tabularnewline
61 & 0.39207 & 0.784141 & 0.60793 \tabularnewline
62 & 0.38103 & 0.76206 & 0.61897 \tabularnewline
63 & 0.460595 & 0.92119 & 0.539405 \tabularnewline
64 & 0.452251 & 0.904502 & 0.547749 \tabularnewline
65 & 0.40275 & 0.8055 & 0.59725 \tabularnewline
66 & 0.411309 & 0.822619 & 0.588691 \tabularnewline
67 & 0.356006 & 0.712011 & 0.643994 \tabularnewline
68 & 0.370521 & 0.741041 & 0.629479 \tabularnewline
69 & 0.336445 & 0.672891 & 0.663555 \tabularnewline
70 & 0.30952 & 0.61904 & 0.69048 \tabularnewline
71 & 0.260537 & 0.521075 & 0.739463 \tabularnewline
72 & 0.223234 & 0.446468 & 0.776766 \tabularnewline
73 & 0.261334 & 0.522667 & 0.738666 \tabularnewline
74 & 0.235848 & 0.471696 & 0.764152 \tabularnewline
75 & 0.198456 & 0.396912 & 0.801544 \tabularnewline
76 & 0.201459 & 0.402917 & 0.798541 \tabularnewline
77 & 0.173186 & 0.346372 & 0.826814 \tabularnewline
78 & 0.136488 & 0.272977 & 0.863512 \tabularnewline
79 & 0.150972 & 0.301944 & 0.849028 \tabularnewline
80 & 0.117957 & 0.235915 & 0.882043 \tabularnewline
81 & 0.141436 & 0.282872 & 0.858564 \tabularnewline
82 & 0.108821 & 0.217643 & 0.891179 \tabularnewline
83 & 0.309989 & 0.619978 & 0.690011 \tabularnewline
84 & 0.255254 & 0.510508 & 0.744746 \tabularnewline
85 & 0.208336 & 0.416672 & 0.791664 \tabularnewline
86 & 0.162779 & 0.325559 & 0.837221 \tabularnewline
87 & 0.127048 & 0.254097 & 0.872952 \tabularnewline
88 & 0.0999023 & 0.199805 & 0.900098 \tabularnewline
89 & 0.113036 & 0.226071 & 0.886964 \tabularnewline
90 & 0.125867 & 0.251735 & 0.874133 \tabularnewline
91 & 0.204506 & 0.409012 & 0.795494 \tabularnewline
92 & 0.15542 & 0.31084 & 0.84458 \tabularnewline
93 & 0.119521 & 0.239042 & 0.880479 \tabularnewline
94 & 0.123471 & 0.246941 & 0.876529 \tabularnewline
95 & 0.100673 & 0.201346 & 0.899327 \tabularnewline
96 & 0.511513 & 0.976974 & 0.488487 \tabularnewline
97 & 0.417817 & 0.835634 & 0.582183 \tabularnewline
98 & 0.350973 & 0.701946 & 0.649027 \tabularnewline
99 & 0.297542 & 0.595084 & 0.702458 \tabularnewline
100 & 0.220223 & 0.440447 & 0.779777 \tabularnewline
101 & 0.28731 & 0.574621 & 0.71269 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270795&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.943911[/C][C]0.112178[/C][C]0.0560892[/C][/ROW]
[ROW][C]9[/C][C]0.953103[/C][C]0.0937936[/C][C]0.0468968[/C][/ROW]
[ROW][C]10[/C][C]0.965828[/C][C]0.0683432[/C][C]0.0341716[/C][/ROW]
[ROW][C]11[/C][C]0.938063[/C][C]0.123873[/C][C]0.0619366[/C][/ROW]
[ROW][C]12[/C][C]0.966431[/C][C]0.0671385[/C][C]0.0335693[/C][/ROW]
[ROW][C]13[/C][C]0.950303[/C][C]0.0993944[/C][C]0.0496972[/C][/ROW]
[ROW][C]14[/C][C]0.92159[/C][C]0.15682[/C][C]0.0784102[/C][/ROW]
[ROW][C]15[/C][C]0.90682[/C][C]0.18636[/C][C]0.0931799[/C][/ROW]
[ROW][C]16[/C][C]0.91372[/C][C]0.172561[/C][C]0.0862803[/C][/ROW]
[ROW][C]17[/C][C]0.8873[/C][C]0.225399[/C][C]0.1127[/C][/ROW]
[ROW][C]18[/C][C]0.848282[/C][C]0.303435[/C][C]0.151718[/C][/ROW]
[ROW][C]19[/C][C]0.798848[/C][C]0.402304[/C][C]0.201152[/C][/ROW]
[ROW][C]20[/C][C]0.740225[/C][C]0.519551[/C][C]0.259775[/C][/ROW]
[ROW][C]21[/C][C]0.728168[/C][C]0.543664[/C][C]0.271832[/C][/ROW]
[ROW][C]22[/C][C]0.688943[/C][C]0.622115[/C][C]0.311057[/C][/ROW]
[ROW][C]23[/C][C]0.629781[/C][C]0.740439[/C][C]0.370219[/C][/ROW]
[ROW][C]24[/C][C]0.568215[/C][C]0.86357[/C][C]0.431785[/C][/ROW]
[ROW][C]25[/C][C]0.517163[/C][C]0.965674[/C][C]0.482837[/C][/ROW]
[ROW][C]26[/C][C]0.490393[/C][C]0.980786[/C][C]0.509607[/C][/ROW]
[ROW][C]27[/C][C]0.568307[/C][C]0.863386[/C][C]0.431693[/C][/ROW]
[ROW][C]28[/C][C]0.536185[/C][C]0.927629[/C][C]0.463815[/C][/ROW]
[ROW][C]29[/C][C]0.508082[/C][C]0.983836[/C][C]0.491918[/C][/ROW]
[ROW][C]30[/C][C]0.470973[/C][C]0.941947[/C][C]0.529027[/C][/ROW]
[ROW][C]31[/C][C]0.467115[/C][C]0.93423[/C][C]0.532885[/C][/ROW]
[ROW][C]32[/C][C]0.41923[/C][C]0.838459[/C][C]0.58077[/C][/ROW]
[ROW][C]33[/C][C]0.418389[/C][C]0.836778[/C][C]0.581611[/C][/ROW]
[ROW][C]34[/C][C]0.377805[/C][C]0.755611[/C][C]0.622195[/C][/ROW]
[ROW][C]35[/C][C]0.329336[/C][C]0.658671[/C][C]0.670664[/C][/ROW]
[ROW][C]36[/C][C]0.276908[/C][C]0.553816[/C][C]0.723092[/C][/ROW]
[ROW][C]37[/C][C]0.301108[/C][C]0.602217[/C][C]0.698892[/C][/ROW]
[ROW][C]38[/C][C]0.403106[/C][C]0.806212[/C][C]0.596894[/C][/ROW]
[ROW][C]39[/C][C]0.37152[/C][C]0.74304[/C][C]0.62848[/C][/ROW]
[ROW][C]40[/C][C]0.517287[/C][C]0.965426[/C][C]0.482713[/C][/ROW]
[ROW][C]41[/C][C]0.466353[/C][C]0.932706[/C][C]0.533647[/C][/ROW]
[ROW][C]42[/C][C]0.425867[/C][C]0.851735[/C][C]0.574133[/C][/ROW]
[ROW][C]43[/C][C]0.372789[/C][C]0.745577[/C][C]0.627211[/C][/ROW]
[ROW][C]44[/C][C]0.383405[/C][C]0.766809[/C][C]0.616595[/C][/ROW]
[ROW][C]45[/C][C]0.345475[/C][C]0.690949[/C][C]0.654525[/C][/ROW]
[ROW][C]46[/C][C]0.312298[/C][C]0.624595[/C][C]0.687702[/C][/ROW]
[ROW][C]47[/C][C]0.368701[/C][C]0.737401[/C][C]0.631299[/C][/ROW]
[ROW][C]48[/C][C]0.415382[/C][C]0.830764[/C][C]0.584618[/C][/ROW]
[ROW][C]49[/C][C]0.406173[/C][C]0.812345[/C][C]0.593827[/C][/ROW]
[ROW][C]50[/C][C]0.373474[/C][C]0.746947[/C][C]0.626526[/C][/ROW]
[ROW][C]51[/C][C]0.368585[/C][C]0.737171[/C][C]0.631415[/C][/ROW]
[ROW][C]52[/C][C]0.326476[/C][C]0.652953[/C][C]0.673524[/C][/ROW]
[ROW][C]53[/C][C]0.299103[/C][C]0.598205[/C][C]0.700897[/C][/ROW]
[ROW][C]54[/C][C]0.282794[/C][C]0.565589[/C][C]0.717206[/C][/ROW]
[ROW][C]55[/C][C]0.24426[/C][C]0.488519[/C][C]0.75574[/C][/ROW]
[ROW][C]56[/C][C]0.384078[/C][C]0.768155[/C][C]0.615922[/C][/ROW]
[ROW][C]57[/C][C]0.423123[/C][C]0.846247[/C][C]0.576877[/C][/ROW]
[ROW][C]58[/C][C]0.372577[/C][C]0.745154[/C][C]0.627423[/C][/ROW]
[ROW][C]59[/C][C]0.38569[/C][C]0.77138[/C][C]0.61431[/C][/ROW]
[ROW][C]60[/C][C]0.414204[/C][C]0.828407[/C][C]0.585796[/C][/ROW]
[ROW][C]61[/C][C]0.39207[/C][C]0.784141[/C][C]0.60793[/C][/ROW]
[ROW][C]62[/C][C]0.38103[/C][C]0.76206[/C][C]0.61897[/C][/ROW]
[ROW][C]63[/C][C]0.460595[/C][C]0.92119[/C][C]0.539405[/C][/ROW]
[ROW][C]64[/C][C]0.452251[/C][C]0.904502[/C][C]0.547749[/C][/ROW]
[ROW][C]65[/C][C]0.40275[/C][C]0.8055[/C][C]0.59725[/C][/ROW]
[ROW][C]66[/C][C]0.411309[/C][C]0.822619[/C][C]0.588691[/C][/ROW]
[ROW][C]67[/C][C]0.356006[/C][C]0.712011[/C][C]0.643994[/C][/ROW]
[ROW][C]68[/C][C]0.370521[/C][C]0.741041[/C][C]0.629479[/C][/ROW]
[ROW][C]69[/C][C]0.336445[/C][C]0.672891[/C][C]0.663555[/C][/ROW]
[ROW][C]70[/C][C]0.30952[/C][C]0.61904[/C][C]0.69048[/C][/ROW]
[ROW][C]71[/C][C]0.260537[/C][C]0.521075[/C][C]0.739463[/C][/ROW]
[ROW][C]72[/C][C]0.223234[/C][C]0.446468[/C][C]0.776766[/C][/ROW]
[ROW][C]73[/C][C]0.261334[/C][C]0.522667[/C][C]0.738666[/C][/ROW]
[ROW][C]74[/C][C]0.235848[/C][C]0.471696[/C][C]0.764152[/C][/ROW]
[ROW][C]75[/C][C]0.198456[/C][C]0.396912[/C][C]0.801544[/C][/ROW]
[ROW][C]76[/C][C]0.201459[/C][C]0.402917[/C][C]0.798541[/C][/ROW]
[ROW][C]77[/C][C]0.173186[/C][C]0.346372[/C][C]0.826814[/C][/ROW]
[ROW][C]78[/C][C]0.136488[/C][C]0.272977[/C][C]0.863512[/C][/ROW]
[ROW][C]79[/C][C]0.150972[/C][C]0.301944[/C][C]0.849028[/C][/ROW]
[ROW][C]80[/C][C]0.117957[/C][C]0.235915[/C][C]0.882043[/C][/ROW]
[ROW][C]81[/C][C]0.141436[/C][C]0.282872[/C][C]0.858564[/C][/ROW]
[ROW][C]82[/C][C]0.108821[/C][C]0.217643[/C][C]0.891179[/C][/ROW]
[ROW][C]83[/C][C]0.309989[/C][C]0.619978[/C][C]0.690011[/C][/ROW]
[ROW][C]84[/C][C]0.255254[/C][C]0.510508[/C][C]0.744746[/C][/ROW]
[ROW][C]85[/C][C]0.208336[/C][C]0.416672[/C][C]0.791664[/C][/ROW]
[ROW][C]86[/C][C]0.162779[/C][C]0.325559[/C][C]0.837221[/C][/ROW]
[ROW][C]87[/C][C]0.127048[/C][C]0.254097[/C][C]0.872952[/C][/ROW]
[ROW][C]88[/C][C]0.0999023[/C][C]0.199805[/C][C]0.900098[/C][/ROW]
[ROW][C]89[/C][C]0.113036[/C][C]0.226071[/C][C]0.886964[/C][/ROW]
[ROW][C]90[/C][C]0.125867[/C][C]0.251735[/C][C]0.874133[/C][/ROW]
[ROW][C]91[/C][C]0.204506[/C][C]0.409012[/C][C]0.795494[/C][/ROW]
[ROW][C]92[/C][C]0.15542[/C][C]0.31084[/C][C]0.84458[/C][/ROW]
[ROW][C]93[/C][C]0.119521[/C][C]0.239042[/C][C]0.880479[/C][/ROW]
[ROW][C]94[/C][C]0.123471[/C][C]0.246941[/C][C]0.876529[/C][/ROW]
[ROW][C]95[/C][C]0.100673[/C][C]0.201346[/C][C]0.899327[/C][/ROW]
[ROW][C]96[/C][C]0.511513[/C][C]0.976974[/C][C]0.488487[/C][/ROW]
[ROW][C]97[/C][C]0.417817[/C][C]0.835634[/C][C]0.582183[/C][/ROW]
[ROW][C]98[/C][C]0.350973[/C][C]0.701946[/C][C]0.649027[/C][/ROW]
[ROW][C]99[/C][C]0.297542[/C][C]0.595084[/C][C]0.702458[/C][/ROW]
[ROW][C]100[/C][C]0.220223[/C][C]0.440447[/C][C]0.779777[/C][/ROW]
[ROW][C]101[/C][C]0.28731[/C][C]0.574621[/C][C]0.71269[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270795&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270795&T=5

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.9439110.1121780.0560892
90.9531030.09379360.0468968
100.9658280.06834320.0341716
110.9380630.1238730.0619366
120.9664310.06713850.0335693
130.9503030.09939440.0496972
140.921590.156820.0784102
150.906820.186360.0931799
160.913720.1725610.0862803
170.88730.2253990.1127
180.8482820.3034350.151718
190.7988480.4023040.201152
200.7402250.5195510.259775
210.7281680.5436640.271832
220.6889430.6221150.311057
230.6297810.7404390.370219
240.5682150.863570.431785
250.5171630.9656740.482837
260.4903930.9807860.509607
270.5683070.8633860.431693
280.5361850.9276290.463815
290.5080820.9838360.491918
300.4709730.9419470.529027
310.4671150.934230.532885
320.419230.8384590.58077
330.4183890.8367780.581611
340.3778050.7556110.622195
350.3293360.6586710.670664
360.2769080.5538160.723092
370.3011080.6022170.698892
380.4031060.8062120.596894
390.371520.743040.62848
400.5172870.9654260.482713
410.4663530.9327060.533647
420.4258670.8517350.574133
430.3727890.7455770.627211
440.3834050.7668090.616595
450.3454750.6909490.654525
460.3122980.6245950.687702
470.3687010.7374010.631299
480.4153820.8307640.584618
490.4061730.8123450.593827
500.3734740.7469470.626526
510.3685850.7371710.631415
520.3264760.6529530.673524
530.2991030.5982050.700897
540.2827940.5655890.717206
550.244260.4885190.75574
560.3840780.7681550.615922
570.4231230.8462470.576877
580.3725770.7451540.627423
590.385690.771380.61431
600.4142040.8284070.585796
610.392070.7841410.60793
620.381030.762060.61897
630.4605950.921190.539405
640.4522510.9045020.547749
650.402750.80550.59725
660.4113090.8226190.588691
670.3560060.7120110.643994
680.3705210.7410410.629479
690.3364450.6728910.663555
700.309520.619040.69048
710.2605370.5210750.739463
720.2232340.4464680.776766
730.2613340.5226670.738666
740.2358480.4716960.764152
750.1984560.3969120.801544
760.2014590.4029170.798541
770.1731860.3463720.826814
780.1364880.2729770.863512
790.1509720.3019440.849028
800.1179570.2359150.882043
810.1414360.2828720.858564
820.1088210.2176430.891179
830.3099890.6199780.690011
840.2552540.5105080.744746
850.2083360.4166720.791664
860.1627790.3255590.837221
870.1270480.2540970.872952
880.09990230.1998050.900098
890.1130360.2260710.886964
900.1258670.2517350.874133
910.2045060.4090120.795494
920.155420.310840.84458
930.1195210.2390420.880479
940.1234710.2469410.876529
950.1006730.2013460.899327
960.5115130.9769740.488487
970.4178170.8356340.582183
980.3509730.7019460.649027
990.2975420.5950840.702458
1000.2202230.4404470.779777
1010.287310.5746210.71269






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level40.0425532OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 4 & 0.0425532 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270795&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.0425532[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270795&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270795&T=6

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level40.0425532OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}